DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to because FIG. 12C has reference character NP while paragraph [0083] line 6 has reference character NP2. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claims 1-20 have been interpreted under 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) to not invoke 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) claim interpretation.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 4, 5, 7-11, 12, 14, 15, and 17-20 of co-pending Application No. 19/069,248 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because independent claim 1 corresponds to co-pending dependent claim 7 without the limitations of its parent claim, thus, this application’s claim 1 is a broader version of the co-pending application’s claim 7 and independent claim 11 corresponds to co-pending dependent claim 17 without the limitations of its parent claim, thus, this application’s claim 1 is a broader version of the co-pending application’s claim 17.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Refer to the following tables which correspond this application’s claims to the co-pending application’s claims.
Claim number correspondence between this application’s claims filed on 03/04/2025 and co-pending 19/069,248 claims filed on 03/04/2025.
This application
1
2
3
4
5
6
Co-pending
19/069,248
7
8
9
9
9
10
This application
7
8
9
10
Co-pending
19/069,248
1
2
4
5
This application
11
12
13
14
15
16
Co-pending
19/069,248
17
18
19
19
19
20
This application
17
18
19
20
Co-pending
19/069,248
11
12
14
15
Claim text correspondence between this application’s claims filed on 03/04/2025 and co-pending 19/069,248 claims filed on 03/04/2025.
This application, 19/069,242
1. A method related to a data generation framework, comprising:
obtaining a first code from reference data by a style encoder;
obtaining a second code from latent encoding by a mapping network, wherein the first code and the second code are both a style code, and the style code corresponds to at least one style option;
inputting first source data to a generator, and outputting a first output corresponding to the first source data by referring to the style code by the generator; and
inputting the first output to a discriminator, and outputting a second output corresponding to the first output by the discriminator, wherein the second output represents whether the first output corresponds to the at least one style option.
2. The method related to the data generation framework according to claim 1, wherein the generator comprises a first encoder and a first decoder, the first encoder is connected to the first decoder, the first encoder comprises at least one first encoder block, the first decoder comprises at least one first decoder block, and the step of outputting the first output corresponding to the first source data by referring to the style code by the generator comprises:
fusing feature data output by one of the at least one first encoder block and the style code to generate fusion data; and
inputting the fusion data and another feature data to one of the at least one first decoder block, wherein the another feature data is an output of one of the at least one first encoder block or an output of another one of the at least one first decoder block.
3. The method related to the data generation framework according to claim 1, further comprising:
updating at least one of the style encoder, the mapping network, the generator, and the discriminator according to a style error, wherein the style error comprises a style similarity error, and the step of updating the at least one of the style encoder, the mapping network, the generator, and the discriminator according to the style error comprises:
calculating a difference between the two first codes respectively obtained from the first source data and second source data by the style encoder to determine the style similarity error.
4. The method related to the data generation framework according to claim 1, further comprising:
updating at least one of the style encoder, the mapping network, the generator, and the discriminator according to a style error, wherein the style error comprises a source preservation error, and the step of updating the at least one of the style encoder, the mapping network, the generator, and the discriminator according to the style error comprises:
calculating a difference between the first source data and second generated data to determine the source preservation error, wherein the generator outputs third generated data corresponding to the first source data by referring to the style code, the generator outputs the second generated data corresponding to the third generated data by referring to a second style code, the second style code is obtained from second reference data by the style encoder, and the second reference data and the first source data correspond to a same one of the at least one style option.
5. The method related to the data generation framework according to claim 1, further comprising:
updating at least one of the style encoder, the mapping network, the generator, and the discriminator according to a style error, wherein the style error comprises a content comparison error, and the step of updating the at least one of the style encoder, the mapping network, the generator, and the discriminator according to the style error comprises:
calculating a difference between a first feature representation and a second feature representation, and calculating a difference between the second feature representation and a third feature representation to determine the content comparison error, wherein the first feature representation is obtained from the first source data by a first encoder of the generator, the generator outputs third generated data corresponding to the first source data by referring to the style code, the second feature representation is obtained from the third generated data by the first encoder, and the third feature representation is obtained from the reference data by the first encoder.
6. The method related to the data generation framework according to claim 1, further comprising:
obtaining a third code from second latent encoding by the mapping network; and
inputting first generated data or third source data to the trained generator, and outputting fourth generated data corresponding to the first generated data or the third source data by referring to the third code by the trained generator
7. The method related to the data generation framework according to claim 1, further comprising:
obtaining a latent representation from training data by an initial encoder;
combining the latent representation and first noise data to generate a noisy latent representation;
inputting the noisy latent representation to a prediction model, and outputting an initial prediction corresponding to the noisy latent representation by referring to first semantic mask data by the prediction model, wherein the first semantic mask data defines at least one first semantic category for the training data; and
updating the prediction model according to a prediction error between the initial prediction and the first noise data to generate a trained prediction model.
8. The method related to the data generation framework according to claim 7, wherein the prediction model comprises a second encoder and a second decoder, the second encoder is connected to the second decoder, the second encoder comprises at least one second encoder block, the second decoder comprises at least one second decoder block, and the step of outputting the initial prediction corresponding to the noisy latent representation by referring to the first semantic mask data by the prediction model comprises:
inputting the noisy latent representation to the second encoder;
inputting the first semantic mask data and feature data to one of the at least one second decoder block, wherein the feature data is an output of one of the at least one second encoder block or an output of another one of the at least one second decoder block; and
outputting the initial prediction by the second decoder.
9. The method related to the data generation framework according to claim 7, wherein the prediction error is a sum of a first error and a second error, the initial prediction comprises a predicted mean and a predicted variation, and the step of updating the prediction model according to the prediction error between the initial prediction and the first noise data comprises:
calculating a difference between the predicted mean and a mean of the first noise data to determine the first error; and
calculating a difference between the predicted variation and a variation of the first noise data to determine the second error.
10. The method related to the data generation framework according to claim 7, further comprising:
inputting second noise data to the trained prediction model, and outputting predicted noise corresponding to the second noise data by referring to second semantic mask data by the trained prediction model, wherein the second semantic mask data defines at least one second semantic category for first generated data;
generating noise removed data according to a difference between the predicted noise and the second noise data; and
converting the noise removed data into the first generated data by a decoder corresponding to the initial encoder.
11. An apparatus related to a data generation framework, comprising:
a storage, used to store a program code; and
a processor, coupled to the storage and configured to load the program code to execute:
obtaining a first code from reference data by a style encoder;
obtaining a second code from latent encoding by a mapping network, wherein the first code and the second code are both a style code, and the style code corresponds to at least one style option;
inputting first source data to a generator, and outputting a first output corresponding to the first source data by referring to the style code by the generator; and
inputting the first output to a discriminator, and outputting a second output corresponding to the first output by the discriminator, wherein the second output represents whether the first output corresponds to the at least one style option.
12. The apparatus related to the data generation framework according to claim 11, wherein the prediction model comprises a first encoder and a first decoder, the first encoder is connected to the first decoder, the first encoder comprises at least one first encoder block, the first decoder comprises at least one first decoder block, and the processor is further configured to:
fuse feature data output by one of the at least one first encoder block and the style code to generate fusion data; and
input the fusion data and another feature data to one of the at least one first decoder block, wherein the another feature data is an output of one of the at least one first encoder block or an output of another one of the at least one first decoder block.
13. The apparatus related to the data generation framework according to claim 11, wherein the processor is further configured to:
update at least one of the style encoder, the mapping network, the generator, and the discriminator according to a style error, wherein the style error comprises a style similarity error, and the step of updating the at least one of the style encoder, the mapping network, the generator, and the discriminator according to the style error comprises:
calculating a difference between the two first codes respectively obtained from the first source data and second source data by the style encoder to determine the style similarity error.
14. The apparatus related to the data generation framework according to claim 11, wherein the processor is further configured to:
update at least one of the style encoder, the mapping network, the generator, and the discriminator according to a style error, wherein the style error comprises a source preservation error, and the step of updating the at least one of the style encoder, the mapping network, the generator, and the discriminator according to the style error comprises:
calculating a difference between the first source data and second generated data to determine the source preservation error, wherein the generator outputs third generated data corresponding to the first source data by referring to the style code, the generator outputs the second generated data corresponding to the third generated data by referring to a second style code, the second style code is obtained from second reference data by the style encoder, and the second reference data and the first source data correspond to a same one of the at least one style option.
15. The apparatus related to the data generation framework according to claim 11, wherein the processor is further configured to:
update at least one of the style encoder, the mapping network, the generator, and the discriminator according to a style error, wherein the style error comprises a content comparison error, and the step of updating the at least one of the style encoder, the mapping network, the generator, and the discriminator according to the style error comprises:
calculating a difference between a first feature representation and a second feature representation, and calculating a difference between the second feature representation and a third feature representation to determine the content comparison error, wherein the first feature representation is obtained from the first source data by a first encoder of the generator, the generator outputs third generated data corresponding to the first source data by referring to the style code, the second feature representation is obtained from the third generated data by the first encoder, and the third feature representation is obtained from the reference data by the first encoder.
16. The apparatus related to the data generation framework according to claim
11, wherein the processor is further configured to:
obtain a third code from second latent encoding by the mapping network; and
input first generated data or third source data to the trained generator, and outputting fourth generated data corresponding to the first generated data or the third source data by referring to the third code by the trained generator.
17. The apparatus related to the data generation framework according to claim 11, wherein the processor is further configured to:
obtain a latent representation from training data by an initial encoder;
combine the latent representation and first noise data to generate a noisy latent representation;
input the noisy latent representation to a prediction model, and output an initial prediction corresponding to the noisy latent representation by referring to first semantic mask data by the prediction model, wherein the first semantic mask data defines at least one first semantic category for the training data; and
update the prediction model according to a prediction error between the initial prediction and the first noise data to generate a trained prediction model.
18. The apparatus related to the data generation framework according to claim 17, wherein the prediction model comprises a second encoder and a second decoder, the second encoder is connected to the second decoder, the second encoder comprises at least one second encoder block, the second decoder comprises at least one second decoder block, and the processor is further configured to:
input the noisy latent representation to the second encoder;
input the first semantic mask data and feature data to one of the at least one second decoder block, wherein the feature data is an output of one of the at least one second encoder block or an output of another one of the at least one second decoder block; and
outputting the initial prediction by the second decoder.
19. The apparatus related to the data generation framework according to claim 17, wherein the prediction error is a sum of a first error and a second error, the initial prediction comprises a predicted mean and a predicted variation, and the processor is further configured to:
calculate a difference between the predicted mean and a mean of the first noise data to determine the first error; and
calculate a difference between the predicted variation and a variation of the first noise data to determine the second error.
20. The apparatus related to the data generation framework according to claim 17, wherein the processor is further configured to:
input second noise data to the trained prediction model, and output predicted noise corresponding to the second noise data by referring to second semantic mask data by the trained prediction model, wherein the second semantic mask data defines at least one second semantic category for first generated data;
generate noise removed data according to a difference between the predicted noise and the second noise data; and
convert the noise removed data into the first generated data by a decoder corresponding to the initial encoder.
Co-pending application, 19/069,248
7. The method related to the data generation framework according to claim 5, further comprising:
obtaining a first code from reference data by a style encoder;
obtaining a second code from latent encoding by a mapping network, wherein the first code and the second code form a style code, and the style code corresponds to at least one style option;
inputting first source data to a generator, and outputting a first output corresponding to the first source data by referring to the style code by the generator; and
inputting the first output to a discriminator, and outputting a second output corresponding to the first output by the discriminator, wherein the second output comprises real or fake corresponding to the at least one style option.
8. The method related to the data generation framework according to claim 7, wherein the generator comprises a second encoder and a second decoder, the second encoder is connected to the second decoder, the second encoder comprises at least one second encoder block, the second decoder comprises at least one second decoder block, and the step of outputting the first output corresponding to the first source data by referring to the style code by the generator comprises:
fusing feature data output by one of the at least one second encoder block and the style code to generate fusion data; and
inputting the fusion data and another feature data to one of the at least one second decoder block, wherein the another feature data is an output of one of the at least one second encoder block or an output of another one of the at least one second decoder block.
9. The method related to the data generation framework according to claim 7, further comprising:
updating at least one of the style encoder, the mapping network, the generator, and the discriminator according to a style error, wherein the style error comprises a style similarity error, a source preservation error, and a content comparison error, and the step of updating the at least one of the style encoder, the mapping network, the generator, and the discriminator according to the style error comprises:
calculating a difference between the two first codes respectively obtained from the first source data and second source data by the style encoder to determine the style similarity error;
calculating a difference between the first source data and second generated data to determine the source preservation error, wherein the generator outputs third generated data corresponding to the first source data by referring to the style code, the generator outputs the second generated data corresponding to the third generated data by referring to a second style code, the second style code is obtained from second reference data by the style encoder, and the second reference data and the first source data correspond to a same one of the at least one style option; and
calculating a difference between a first feature representation and a second feature representation, and calculating a difference between the second feature representation and a third feature representation to determine the content comparison error, wherein the first feature representation is obtained from the first source data by a second encoder of the generator, the second feature representation is obtained from the third generated data by the second encoder, and the third feature representation is obtained from the reference data by the second encoder.
10. The method related to the data generation framework according to claim 7, further comprising:
obtaining a third code from second latent encoding by the mapping network; and
inputting the first generated data to the trained generator, and outputting fourth generated data corresponding to the first generated data by referring to the third code by the trained generator.
1. A method related to a data generation framework, implemented by a processor, the method comprising:
obtaining a latent representation from training data by an initial encoder;
combining the latent representation and first noise data to generate a noisy latent representation;
inputting the noisy latent representation to a prediction model, and outputting an initial prediction corresponding to the noisy latent representation by referring to first semantic mask data by the prediction model, wherein the first semantic mask data defines at least one first semantic category for the training data; and
updating the prediction model according to a prediction error between the initial prediction and the first noise data to generate a trained prediction model.
2. The method related to the data generation framework according to claim 1, wherein the prediction model comprises a first encoder and a first decoder, the first encoder is connected to the first decoder, the first encoder comprises at least one first encoder block, the first decoder comprises at least one first decoder block, and the step of outputting the initial prediction corresponding to the noisy latent representation by referring to the first semantic mask data by the prediction model comprises:
inputting the noisy latent representation to the first encoder;
inputting the first semantic mask data and feature data to one of the at least one first decoder block, wherein the feature data is an output of one of the at least one first encoder block or an output of another one of the at least one first decoder block; and
outputting the initial prediction by the first decoder.
4. The method related to the data generation framework according to claim 1, wherein the prediction error is a sum of a first error and a second error, the initial prediction comprises a predicted mean and a predicted variation, and the step of updating the prediction model according to the prediction error between the initial prediction and the first noise data comprises:
calculating a difference between the predicted mean and a mean of the first noise data to determine the first error; and
calculating a difference between the predicted variation and a variation of the first noise data to determine the second error.
5. The method related to the data generation framework according to claim 1, further comprising:
inputting second noise data to the trained prediction model, and outputting predicted noise corresponding to the second noise data by referring to second semantic mask data by the trained prediction model, wherein the second semantic mask data defines at least one second semantic category for first generated data;
generating noise removed data according to a difference between the predicted noise and the second noise data; and
converting the noise removed data into the first generated data by a decoder corresponding to the initial encoder.
17. The apparatus related to the data generation framework according to claim15,wherein the processor is further configured to:
obtain a first code from reference data by a style encoder;
obtain a second code from latent encoding by a mapping network, wherein the first code and the second code form a style code, and the style code corresponds to at least one style option;
input first source data to a generator, and output a first output corresponding to the first source data by referring to the style code by the generator; and
input the first output to a discriminator, and output a second output corresponding to the first output by the discriminator, wherein the second output comprises real or fake corresponding to the at least one style option.
18. The apparatus related to the data generation framework according to claim 17, wherein the generator comprises a second encoder and a second decoder, the second encoder is connected to the second decoder, the second encoder comprises at least one second encoder block, the second decoder comprises at least one second decoder block, and the processor is further configured to:
fuse feature data output by one of the at least one second encoder block and the style code to generate fusion data; and
input the fusion data and another feature data to one of the at least one second decoder block, wherein the another feature data is an output of one of the at least one second encoder block or an output of another one of the at least one second decoder block.
19. The apparatus related to the data generation framework according to claim 17, wherein the processor is further configured to:
update at least one of the style encoder, the mapping network, the generator, and the discriminator according to a style error, wherein the style error comprises a style similarity error, a source preservation error, and a content comparison error, and the step of updating the at least one of the style encoder, the mapping network, the generator, and:
calculate a difference between the two first codes respectively obtained from the first source data and second source data by the style encoder to determine the style similarity error;
calculate a difference between the first source data and second generated data to determine the source preservation error, wherein the generator outputs third generated data corresponding to the first source data by referring to the style code, the generator outputs the second generated data corresponding to the third generated data by referring to a second style code, the second style code is obtained from second reference data by the style encoder, and the second reference data and the first source data correspond to a same one of the at least one style option; and
calculate a difference between a first feature representation and a second feature representation, and calculate a difference between the second feature representation and a third feature representation to determine the content comparison error, wherein the first feature representation is obtained from the first source data by a second encoder of the generator, the second feature representation is obtained from the third generated data by the second encoder, and the third feature representation is obtained from the reference data by the second encoder.
20. The apparatus related to the data generation framework according to claim 17, wherein the processor is further configured to:
obtain a third code from second latent encoding by the mapping network; and
input the first generated data to the trained generator, and output fourth generated data corresponding to the first generated data by referring to the third code by the trained generator.
11. An apparatus related to a data generation framework, comprising:
a storage, used to store a program code; and
a processor, coupled to the storage and configured to load the program code to execute:
obtaining a latent representation from training data by an initial encoder;
combining the latent representation and first noise data to generate a noisy latent representation;
inputting the noisy latent representation to a prediction model, and outputting an initial prediction corresponding to the noisy latent representation by referring to first semantic mask data by the prediction model, wherein the first semantic mask data defines at least one first semantic category for the training data; and
updating the prediction model according to a prediction error between the initial prediction and the first noise data to generate a trained prediction model.
12. The apparatus related to the data generation framework according to claim 11, wherein the prediction model comprises a first encoder and a first decoder, the first encoder is connected to the first decoder, the first encoder comprises at least one first encoder block, the first decoder comprises at least one first decoder block, and the processor is further configured to:
input the noisy latent representation to the first encoder;
input the first semantic mask data and feature data to one of the at least one first decoder block, wherein the feature data is an output of one of the at least one first encoder block or an output of another one of the at least one first decoder block; and
output the initial prediction by the first decoder.
14. The apparatus related to the data generation framework according to claim 11, wherein the prediction error is a sum of a first error and a second error, the initial prediction comprises a predicted mean and a predicted variation, and the processor is further configured to:
calculate a difference between the predicted mean and a mean of the first noise data to determine the first error; and
calculate a difference between the predicted variation and a variation of the first noise data to determine the second error.
15. The apparatus related to the data generation framework according to claim 11, wherein the processor is further configured to:
input second noise data to the trained prediction model, and output predicted noise corresponding to the second noise data by referring to second semantic mask data by the trained prediction model, wherein the second semantic mask data defines at least one second semantic category for first generated data;
generate noise removed data according to a difference between the predicted noise and the second noise data; and
convert the noise removed data into the first generated data by a decoder corresponding to the initial encoder.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Song et al., US Patent Application Publication No. 2024/0273871, describes an encoder 330, a latent space 335, a style conditioned three-dimensional generator 340, a decoder 350, a style prior generator 370, and a discriminator 380 in which the discriminator 380 may be configured to use adversarial loss to tune the style conditioned three-dimensional generator 340 by comparing a distribution of translated images of the three-dimensional stylized portraits from the style conditioned three-dimensional generator 340 to a distribution of the two-dimensional stylized portraits generated by the style prior generator 370, e.g., of training data., refer to paragraphs [0051]-[0058].
Wang et al., US Patent Application Publication No. 2023/0095041, describes that the generator (1) and the Mapping network (3) and the Style-Content encoder (4) minimize the sum of the losses and the discriminator (2) maximizes the losses to carry out min-max optimization, refer to paragraphs [0021] and [0100] and claims 1, 8, 13, and 14.
Gottlieb, US Patent No. 11,341,699, describes body style characteristics of a vehicle, refer to column 3 line 11 to column 4 line 21.
Zhang et al., CN 110992252 A, describes true and false discriminator judging generation image and the real image, the Self-Attention embedded in the discriminator.
Allowable Subject Matter
Claims 1-20 would be allowable if a proper terminal disclaimer is filed or if rewritten or amended to overcome the provisionally rejected on the ground of nonstatutory double patenting rejection(s) set forth in this Office action.
The prior art of record fails to teach or suggest in the context of each of independent claims 1 and 11:
obtaining a first code from reference data by a style encoder (style code 912 from Style encoder (E) refer to FIG. 9);
obtaining a second code from latent encoding by a mapping network, wherein the first code and the second code are both a style code, and the style code corresponds to at least one style option (style code 912 from Mapping Network (M), refer to FIG. 9, wherein style options E11 to E13 from Style encoder (E) and style options M11 to M13 from Mapping Network (M) correspond to style code 912);
inputting first source data to a generator, and outputting a first output corresponding to the first source data by referring to the style code by the generator (Generator(G), refer to FIG. 9); and
inputting the first output to a discriminator, and outputting a second output corresponding to the first output by the discriminator, wherein the second output represents whether the first output corresponds to the at least one style option (FIG. 8, S840;, Discriminator(D), at least one style option: Real or Fake output from Discriminator(D) represents whether output of Generator(G) corresponds to style option(s), refer to FIG. 9.) (emphasis added and explanation with reference to drawings and paragraphs added).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEFFERY A BRIER whose telephone number is (571)272-7656. The examiner can normally be reached on Mon-Fri from 8:30am-3:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao M Wu, can be reached at telephone number 571-272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
JEFFERY A. BRIER
Primary Examiner
Art Unit 2613
/JEFFERY A BRIER/Primary Examiner, Art Unit 2613
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